Research workers in many of the life sciences are realizing the substantial limitations of statistical tests, test statistics, arbitrary alpha levels, P-values, and the dichotomous rulings concerning "statistical significance." These traditional approaches were developed at the beginning of the last century and are being replaced by modern methods that are much more useful. They provide easy-to-compute quantities such as the probability of each hypothesis/model and measures of formal evidence. Furthermore, simple methods allow formal inference (e.g., prediction/forecasting) from all the hypotheses/models in the a priori set (multimodel inference).

I am planning to offer several 1-day courses on the Information-Theoretic approaches to statistical inference during the fall months, 2015. These courses focus on the practical application of these new methods and are based on Kullback-Leibler information and Akaike's information criterion (AIC). The material follows the recent textbook,

Anderson. D. R.2008. Model based inference in the life sciences: a primer on evidence. Springer, New York, NY. 184pp.

These courses stress science and science philosophy as much as statistical methods. The focus is on quantification and qualification of formal evidence concerning alternative science hypotheses.

These courses are hosted, organized and delivered at your university, agency, institute or training center. I have given nearly 70 of these courses and they have been well received. The courses are informal and discussion and debate are encouraged. Further insights can be found at